The increasing presence of renewable energy plants has created new challenges such as
grid integration, load balancing and energy trading, making it fundamental to provide …

In renewable energy forecasting, data are typically collected by geographically distributed
sensor networks, which poses several issues.(i) Data represent physical properties that are …

Frequent pattern mining over data streams is currently one of the most interesting fields in
data mining. Current databases have needed more immediate processes since enormous …

The increasing availability and use of positioning devices has resulted in large volumes of
trajectory data. However, semantic annotations for such data are typically added by domain …

Spatial autocorrelation is the correlation among data values which is strictly due to the
relative spatial proximity of the objects that the data refer to. Inappropriate treatment of data …

The motivation for regional association rule mining and scoping is driven by the facts that
global statistics seldom provide useful insight and that most relationships in spatial datasets …

Co-location mining is a classical problem in spatial pattern mining. Considering a set of
boolean spatial features, the goal is to find subsets of features frequently located together. It …

During the last decade, data miners became aware of geographical data. Today, knowledge
discovery from geographic data is still an open research field but promises to be a solid …

The association rule mining technique emerged with the objective to find novel, useful, and
previously unknown associations from transactional databases, and a large amount of …

Spatial association mining has been used for discovering frequent spatial association
patterns from large static spatial databases. When a large spatial database is updated, it is …